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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
medicine
Development and internal validation of clinical prediction models for outcomes of complicated intra-abdominal infection
British Journal of Surgery, Volume 108, No. 4, Year 2021
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Description
Background: Complicated intra-abdominal infections (cIAIs) are associated with significant morbidity and mortality. The aim of this study was to describe the clinical characteristics of patients with cIAI in a multicentre study and to develop clinical prediction models (CPMs) to help identify patients at risk of mortality or relapse. Methods: A multicentre observational study was conducted from August 2016 to February 2017 in the UK. Adult patients diagnosed with cIAI were included. Multivariable logistic regression was performed to develop CPMs for mortality and cIAI relapse. The c-statistic was used to test model discrimination. Model calibration was tested using calibration slopes and calibration in the large (CITL). The CPMs were then presented as point scoring systems and validated further. Results: Overall, 417 patients from 31 surgical centres were included in the analysis. At 90 days after diagnosis, 17.3 per cent had a cIAI relapse and the mortality rate was 11.3 per cent. Predictors in the mortality model were age, cIAI aetiology, presence of a perforated viscus and source control procedure. Predictors of cIAI relapse included the presence of collections, outcome of initial management, and duration of antibiotic treatment. The c-statistic adjusted for model optimism was 0.79 (95 per cent c.i. 0.75 to 0.87) and 0.74 (0.73 to 0.85) for mortality and cIAI relapse CPMs. Adjusted calibration slopes were 0.88 (95 per cent c.i. 0.76 to 0.90) for the mortality model and 0.91 (0.88 to 0.94) for the relapse model; CITL was -0.19 (95 per cent c.i. -0.39 to -0.12) and - 0.01 (- 0.17 to -0.03) respectively. Conclusion: Relapse of infection and death after complicated intra-abdominal infections are common. Clinical prediction models were developed to identify patients at increased risk of relapse or death after treatment, these now require external validation. © 2021 The Author(s).
Authors & Co-Authors
Bonnett, Laura Jayne
United Kingdom, Liverpool
University of Liverpool
Aggarwal, Ila
United Kingdom, Dundee
Nhs Tayside
Goodman, Anna L.
United Kingdom, London
Guy's and st Thomas' Nhs Foundation Trust
Halai, Sonal M.
United Kingdom, London
The Lister Hospital
HASHEM, Mohamed
United Kingdom, Maidstone
Maidstone and Tunbridge Wells Nhs Trust
Hodgson, Susanne Helena C.
United Kingdom, Oxford
Oxford University Hospitals Nhs Foundation Trust
Jarchow-MacDonald, Anna Amrit
United Kingdom, Dundee
Nhs Tayside
Kailavasan, Mithun M.
United Kingdom, Leicester
University Hospitals of Leicester Nhs Trust
Lawday, Samuel
United Kingdom, Exeter
Royal Devon and Exeter Nhs Foundation Trust
Mabayoje, Diana Ayoola
United Kingdom, London
Barts Health Nhs Trust
Narula, Harjeet Singh
United Kingdom, Chesterfield
Chesterfield Royal Hospital Nhs Foundation Trust
Scarborough, Claire
United Kingdom, Oxford
Oxford University Hospitals Nhs Foundation Trust
Tilley, Robert A.F.
United Kingdom, Plymouth
University Hospitals Plymouth Nhs Trust
Kirby, Andrew N.
United Kingdom, Leeds
Leeds Teaching Hospitals Nhs Trust
United Kingdom, Stockton on Tees
University Hospital of North Tees
Statistics
Citations: 1
Authors: 14
Affiliations: 30
Identifiers
Doi:
10.1093/bjs/znaa117
ISSN:
00071323
Research Areas
Health System And Policy